Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 10 of 10 for “"Graph convolutional neural network"”.
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Unsupervised Feature Learning for Point Cloud by Contrasting and Clustering with Graph Convolutional Neural Network
<p>Recently, deep graph neural networks (GNNs) have attracted significant attention for point cloud understanding tasks, including classification, segmentation, and detection. However, the training of such deep networks still requires a large amount of annotated data, which is both expensive and …
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A Graph Convolutional Neural Network Based Approach for Object Tracking Using Augmented Detections With Optical Flow
… for online Multi-Object Tracking (MOT) using Graph Convolutional Neural Network (GCNN) based feature extraction and end-to-end feature matching for object association. The Graph based approach incorporates both appearance and geometry of objects at past frames as well as the current frame into …
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Attributed Graph Classification via Deep Graph Convolutional Neural Networks
From social networks to biological networks, graphs are a natural way to represent a diverse set of real-world data. This research presents attributed graph convolutional neural network with a pooling layer (AGCP for short), a novel end-to-end deep neural network model which captures the …
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Prédiction de la tendance des actions basée sur les réseaux convolutifs graphiques et les LSTM
… The proposed prediction method is based on Graph Convolutional Neural Network for clustering and Long Short-Term Memory model for prediction. This method is suitable for the data clustering of unbalanced classes too. The experiments on real-world stock data demonstrate that our method can …
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Supervised Inference of Gene Regulatory Networks
A gene regulatory network (GRN) records the interactions among transcription factors and their target genes. GRNs are useful to study how transcription factors (TFs) control gene expression as cells transition between states during differentiation and development. Scientists usually construct GRNs …
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Brain Network Connectivity in Anaesthesia and Disorders of Consciousness
… (fMRI) data pre-processing to measure brain network connectivity more accurately. This pre-processing method is then applied to the analyses in the remainder of the thesis. Experiment 2 focuses on a fMRI dataset in which healthy volunteers were administered propofol, an anaesthetic drug known …
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Algorithms for regulatory network inference and experiment planning in systems biology
… mathematical model of the protein regulatory network controlling cell division in budding yeast. (ii) I formulate several natural problems related to efficient synthesis of a target mutant from source mutants. These formulations capture experimentally-useful notions of verifiability (e.g., the …
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Spatiotemporal Event Forecasting and Analysis with Ubiquitous Urban Sensors
… is the subject of this dissertation. A graph convolutional neural network for crime prediction, a multitask learning system for traffic incident prediction with spatiotemporal feature learning, social media-based transportation event detection, and a graph convolutional network-based …
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Deep learning of proteomics data
… new state-of-the-art by using large unsupervised networks. In this chapter, we leverage a BERT model that has been pre-trained on a vast quantity of proteomic data, to model a collection of regression tasks using only a minimal amount of data. We adopt a triplet network structure to fine-tune the …
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Learning robust and efficient point cloud representations
L'abstract è presente nell'allegato / the abstract is in the attachment